The performance of TAGI is now compared with PBP [1]}, VMG [2]}, and MC-dropout [3]} using benchmark regression datasets. For the purpose of comparison with the results from other methods, all the datasets are analyzed for a fixed number of epochs, that is \(\mathtt {E}=40\) . For all cases, the data is normalized, the activation function is a ReLU, and the batch size is one, i.e. the inference is performed using one observation at a time; The prior covariance for bias is initialized to \(\Sigma _{{B}}^{0}=0.01\cdot \mathbf {I}\) , and for weights \(\Sigma _{{W}}^{0}\) , by multiplying the Xaviers's approach [4]} by a factor 0.25; The initial value for the observation error's standard deviation is set to \(\sigma _{V}=1\) , and this value is optimized using a 5-fold cross-validation setup.
